Using plasma proteomic pattern for diagnosis, classification, prediction of response to therapy and clinical behavior, stratification of therapy, and monitoring disease in hematologic malignancies

Abstract

The present invention demonstrates that the diagnosis and prediction of clinical behavior in patients with hematologic malignancies, such as leukemia, can be accomplished by analysis of proteins present in a plasma sample. Thus, in particular embodiments the present invention uses plasma to create a diagnostic or prognostic protein profile of a hematologic malignancy comprising collecting plasma samples from a population of patients with hematologic malignancies; generating protein spectra from the plasma samples with or without fractionation; comparing the protein spectra with clinical data; and identifying protein markers in the plasma samples that correlate with the clinical data. Protein markers identified by this approach can then be used to create a protein profile that can be used to diagnose the hematologic malignancy or determine the prognosis of the hematologic malignancy. Potentially these specific proteins can be identified and targeted in the therapy of these malignancies.

1. A method of predicting response to therapy or an increased risk of relapse following therapy in a patient with acute myeloid leukemia (AML) comprising:
 (a) obtaining a plasma sample from the patient; 
 (b) performing mass spectrometry on the plasma sample to generate a protein spectra comprising protein peaks; 
 (c) identifying a protein peak or group of protein peaks in the protein spectra corresponding to one or more of Peak 1 (2533.253 Daltons), Peak 2 (12801.17 Daltons), Peak 3 (944.0915 Daltons), Peak 4 (11095.88 Daltons), Peak 5 (2648.984 Daltons), Peak 6 (13506.15 Daltons), Peak 7 (12687.09 Daltons), Peak 8 (12519.29 Daltons), Peak 9 (207.8056 Daltons), Peak 10 (40019.19 Daltons), Peak 11 (12241.71 Daltons), Peak 12 (26397.83 Daltons), Peak 13 (3223.238 Daltons), Peak 14 (895.5696 Daltons), Peak 15 (2675.053 Daltons), Peak 16 (518.8676 Daltons), Peak 17 (876.7685 Daltons), Peak 18 (12139.4335 Daltons), Peak 19 (11677.6762 Daltons), Peak 20 (11483.9713 Daltons), Peak 21 (11322.1079 Daltons), Peak 22 (11095.8768 Daltons), Peak 23 (7831.326 Daltons), Peak 24 (11481.7153 Daltons), Peak 25 (12235.8865 Daltons), Peak 26 (797.602 Daltons), Peak 27 (783.9856 Daltons), Peak 28 (11884.4738 Daltons), or Peak 29 (2507.8862 Daltons); and 
 (d) predicting the patient's response to therapy or increased risk of relapse following therapy based on the identification of one or more of Peaks 1 to 29, wherein Peaks 1 to 17 are predictive of a response to therapy and Peaks 18 to 29 are predictive of an increased risk of relapse following therapy. 
 
     
     
       2. The method of  claim 1 , further comprising fractionating the plasma sample. 
     
     
       3. The method of  claim 1 , wherein the mass spectrometry is surface-enhance laser desorption/ionization (SELDI) mass spectrometry. 
     
     
       4. The method of  claim 1 , wherein the therapy is idarubicin and ara-C. 
     
     
       5. The method of  claim 1 , wherein the therapy is Gleevec (imatinib mesylate). 
     
     
       6. The method of  claim 1 , wherein Peak 1 (2533.253 Daltons), Peak 2 (12801.17 Daltons), Peak 3 (944.0915 Daltons), Peak 4 (11095.88 Daltons), Peak 5 (2648.984 Daltons), Peak 6 (13506.15 Daltons), Peak 7 (12687.09 Daltons), Peak 8 (12519.29 Daltons), Peak 9 (207.8056 Daltons), Peak 10 (40019.19 Daltons), Peak 11 (12241.71 Daltons), Peak 12 (26397.83 Daltons), Peak 13 (3223.238 Daltons), Peak 14 (895.5696 Daltons), Peak 15 (2675.053 Daltons), Peak 16 (518.8676 Daltons), or Peak 17 (876.7685 Daltons). 
     
     
       7. The method of  claim 1 , wherein identifying the protein peak or group of protein peaks comprises identifying Peak 1 (2533.253 Daltons). 
     
     
       8. The method of  claim 7 , further comprising identifying Peak 2 (12801.17 Daltons).